Learn How to Make AI-Driven Decisions

The Pre-Work gets you onboarded into the world of Data Science, Artificial Intelligence, and Generative AI, where they came from, how they are used in the industry, and how business problems are typically solved using them, thereby preparing you to explore the transformative potential of data-driven decision making and equipping you with essential programming skills. This will allow you to get to zoom out and see the big picture before you start learning the details.

This module gives you an overview of the complete lifecycle of an AI application through a comprehensive case study analysis. By examining real-world scenarios, you will gain a holistic understanding of how AI is leveraged to address and solve complex business challenges. This detailed walkthrough will equip you with the insights needed to see the broader context of AI's role in driving business solutions, from conception to execution.

This course will aim to build the programming and statistical foundations necessary for the rest of the program. It will cover the concepts, tools, and techniques required to effectively learn and implement the ideas presented in subsequent courses. 

(Numpy arrays and Functions, Pandas Series and DataFrames, Pandas Functions, Saving and loading datasets using Pandas. Data Visualization using Seaborn, Matplotlib, and Plotly. Introduction to Inferential Statistics, Fundamentals of Probability Distributions, The Central Limit Theorem, Hypothesis Testing, Univariate Analysis, Bivariate Analysis, Missing Value Treatment, Outlier Treatment)

This course equips you with the tools and techniques to harness a vast amount of unstructured data and uncover hidden patterns that can enhance performance in various fields. 

(Supervised & Unsupervised Learning: Understanding Classification and Clustering Methods. K-Means Clustering, Dimensionality Reduction Techniques: PCA and t-SNE)

In this course, you will delve into the world of predictive modeling using both classical and cutting-edge regression techniques. This course builds upon foundational knowledge to equip you with skills to analyze and predict using data, whether the focus is on understanding past data trends or forecasting future outcomes. The emphasis will be on identifying the relationship between inputs and outputs and practically applying these insights. The course will not only highlight traditional linear and non-linear regression methods but will also introduce modern approaches suited for high-dimensional datasets. You'll gain an understanding of causal inference, enabling you to differentiate between correlation and causation in your predictive models. 

(Linear and Non-Linear Regression, Causal Inference, Regression with High-Dimensional Data, Regularization Techniques, Model Evaluation, Cross-Validation, and Bootstrapping)

This course dives into essential techniques of classification to determine the class of observations, utilizing tree-based algorithms like Decision Trees and Random Forests. Additionally, gain insights into Hypothesis Testing to make informed inferences about population parameters. Through this course, you will learn to efficiently address diverse data science challenges and enhance decision-making processes using statistical tests and classification models. 

(Introduction to Classification, Logistic Regression, Decision Trees, and Random Forest, Type 1 Error & Type 2 Error in Classification Problems, Hypothesis Testing)

In this course, you will dive deep into the world of Deep Learning, a transformative technology that outperforms classical machine learning techniques by handling complex unstructured data. You will explore the fundamental concepts of representation learning and Neural Networks, understanding how they transcend the constraints of traditional feature engineering. Learn to build and implement Artificial Neural Networks using TensorFlow and Keras, enhancing your ability to solve intricate prediction problems with remarkable accuracy. 

(Introduction to Deep Learning, Neural Network Representations: One Hidden Layer, Hidden Neurons, Multiple Layers & Multi-class Predictions, Introduction to Computer Vision, ANN vs CNN, Basic terminologies related to CNN, CNN architecture, Transfer Learning)

In this comprehensive course, you will explore Recommender Systems, a powerful solution for tackling the problem of information overload faced by users in today's digital age. Learn the foundational principles and the necessity of Recommendation Systems and how they can personalize user experiences by delivering the most relevant content. Delve into various recommendation techniques, from simple solutions using statistical and Machine Learning methods to advanced Collaborative Filtering approaches, designed to analyze user data and provide customized recommendations. 

(Recommendation Systems - Overview & background, Collaborative Filtering & Singular Value Threshold)

This course on Ethical and Responsible AI provides a focused overview of key ethical considerations throughout the AI lifecycle. You'll learn to identify biases, comprehend causality, and safeguard privacy within AI systems. The course also explores the interconnections and interdependencies across AI domains, equipping you to build systems that are ethically sound and aligned with societal values. 

(Introduction to AI Lifecycle, Introduction to Bias and Its Examples, Introduction to Causality and Privacy, Interconnections and Domains, Interdependency and Feedback in AI Systems)

This course will introduce you to how to design and create prompts, various Prompting Techniques, help gain insights on how to interact effectively and elicit the desired responses from LLM models, and various applications and use cases where prompt engineering plays a vital role. 

(Operationalizing Generative AI, LLM Training and Inference, OpenAI Journey and GPT Training, Model Deployment and tokens, Introduction to prompt engineering, Prompt Engineering review and reusable templates, Zero-Shot and Few-Shot Prompting, Chain-of-Thought Prompting, Introduction to APIs for LLMs)

In this course, you'll delve into the fascinating world of networks, an integral component in areas like social networking and gene regulation. While prior modules focused on regression, classification, and recommendation systems, this module shifts the focus to interactions and correlations as primary data or interest points. The course provides a systematic exploration of methods for analyzing complex network structures and inferring unseen data. With an emphasis on graphical models, you'll understand their powerful role in modeling network processes and facilitating statistical computations. Professors Caroline Uhler and Guy Bresler will guide you through foundational network concepts, the applications of network data, and methods for constructing and understanding network behavior, thereby providing you with the tools to leverage networks and graphical models effectively. 

(Introduction to Networks, Network Analysis, Graphical Models)

This course focuses on the integral aspect of temporal data and its significance in predictive modeling. By understanding how data evolves, learners can build robust models to anticipate future trends. Dive into the dynamics of temporal data, learning to define inputs and outputs for superior prediction accuracy. Explore feature engineering techniques that transform temporal data into valuable insights. Evaluate prediction models to ensure they're ready for real-world deployment. This course includes hands-on examples and strategies such as Deep Feature Synthesis, preparing you to effectively apply predictive models in various problem domains. 

(Introduction to Predictive Analytics and Feature Engineering, Deep Feature Synthesis - Primitives and Feature Engineering, Model Selection Techniques, K-Fold Cross Validation)

This course offers a hands-on, layered introduction to the core components behind today’s generative AI systems. You'll begin with the foundations of Generative AI and NLP, then explore how deep learning evolved into powerful transformer-based architectures like GPT. You'll understand how large language models are trained, deployed, and fine-tuned using techniques like prompt engineering, Retrieval-Augmented Generation (RAG), and reinforcement learning. With practical demos, notebooks, and solution walkthroughs, the course equips you with the skills to prototype and optimize your own AI assistants using modern tools. 

(Demystifying Generative AI, Overview of Natural Language Processing, Advancements in AI – From deep learning to transformers and LLMs, Understanding GPT and LLM Training, Hands-on Demonstration – Building a prototypical AI assistant using LLMs and RAG)